Machine Learning Systems, Methods, Components, and Software for Recommending and Ordering Independent Medical Examinations

ABSTRACT

Millions of bodily injury insurance claims are filed yearly to help people who&#39;ve suffered accidents from work, slip-and-falls, and auto collisions. In processing these claims, insurance companies, TPAs, and law firms routinely hire experts, such as physicians, to conduct independent medical evaluations (IMEs) to assist claims adjusters and attorneys in analyzing the eligibility of claimants for indemnity and medical benefit payments. IMEs typically cost thousands of dollars each. Yet, many are ordered too early, wasting money that could otherwise be used to reduce insurance premiums. To reduce this waste, the inventors devised, among other things, one or more exemplary systems which not only predict the outcomes of IMEs based on claimant medical records and/or or activity data before ordering them, but also presents selected claims and predictions within a graphical user interface that facilitates ordering the IMEs from a list of available physicians.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/485,187 filed Sep. 24, 2021, which claims priority to U.S.Provisional Patent Application 63/209,716, filed Jun. 11, 2021, and toU.S. Non-Provisional Patent Application 17,178,055, filed Feb. 17, 2021,which itself claims priority to Provisional Patent Application62/977,696 filed Feb. 17, 2020. All of these applications areincorporated herein by reference in their entirety.

COPYRIGHT NOTICE AND PERMISSION

A portion of this patent document contains material subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent document or the patent disclosure,as it appears in the patent and trademark office patent files orrecords, but otherwise reserves all copyrights whatsoever. The followingnotice applies to this document: Copyright© 2020-21, INFINITINTEL, INC.

TECHNICAL FIELD

Various embodiments of the invention relate generally to systems andmethods for managing bodily injury insurance claims related to workerscompensation, commercial liability, homeowners, and automobile insurancepolicies, particularly methods and processes for ordering independentmedical examinations for such claims.

BACKGROUND

Millions of bodily injury insurance claims are filed every year, manystemming from work accidents, repetitive stresses, slip-and-fallaccidents, and auto collisions. When an injury occurs, the injuredperson, known as a claimant, files a claim to receive benefits under theprovisions of an insurance policy, for example a homeowner or automobileliability policy for personal insurance or a workers compensation orgeneral liability policy for commercial insurance. Wage loss, medicalexpenses, rehabilitation, vocational, and disability benefits are themost often claimed insurance benefits for bodily injuries.

In administering or processing bodily injury claims, insurance companiesand specialty companies that process claims (third-party administrators,or TPAs) employ claims adjusters. Claims adjusters collect and manuallyreview medical records for filed claims and decide whether to accept ordeny the benefit claims. Claims adjusters are also responsible fordocumenting the potential cost of claims to the insurance company. Thiscost estimates, which escalate with prolonged medical treatment expensesand lost-wage payments, are used by insurance company reserve managersto ensure that their insurance companies have sufficient cash reservesto not only cover the claim expenses, but also avoid legal fines forfailure to maintain adequate reserves.

As an aid to determining the cost of claims and whether continuedpayment on open claims is appropriate, claims adjusters typically hirephysicians to conduct independent medical evaluations (IMEs). IMEs helpadjusters determine whether to accept or deny claimant assertionsregarding medical conditions, continued medical care needs, treatmentrecommendations, work disability, work restrictions, and injurycausation. In short, IMEs help adjusters determine when to closeinsurance claims that would otherwise stay open and result in paymentsfor unnecessary medical expenses and wage loss benefits.

IMEs typically entail board-certified physicians studying claimantmedical records, performing physical examinations, and authoring formalwritten reports that legally render and document their professionalmedical opinions regarding the condition and injury of insuranceclaimants. With this level of attention, the cost of each IME typicallycosts an insurer thousands of dollars. For insurers covering hundreds oreven thousands of bodily injury claims every year, some of which mayrequire multiple IMEs before closure, the annual cumulative cost of IMEsis not only significant, but also drives up the cost of providinginsurance and ultimately the insurance premiums paid by businesses andindividuals.

One of the present inventors, a former workers-compensation claimsadjuster and current IME facilitator for insurance companies, hasrecognized that a significant percentage of IMEs indicate that claimantsare not ready to return to work at the time the examination occurs.Indeed, this inventor's research indicates that at least 40% of thecostly IMEs conducted in a particular sample set did not deem theexamined claimant ready to return to work. Moreover, the researchindicated that on average at least two IMEs per claimant were conductedbefore each claimant was released to return to work, again addingsignificantly to the insurance premiums paid by businesses andindividuals in the insurance pool. Furthermore, the highly subjectivenature of the medical records review by claims adjusters leads toinconsistent decisions about IME scheduling and claim administration,both by the same adjuster on similar claims, and across multipleadjusters within an insurance company or claims processer, potentiallyfeeding concerns about administrative impartiality and fairness.

Accordingly, the present inventors—one, the mentioned former claimsadjuster and the other, a data scientist—have recognized a need toimprove the process of scheduling IMEs for bodily injury claims,particularly claims related to automobile, workers compensation, andgeneral liability insurance.

SUMMARY

To address one or more of these and/or other needs or problems, thepresent inventors devised, among other things, one or more exemplarysystems, methods, devices, assemblies, components, and/or software andgraphical interfaces related to, among other things, predicting thereadiness of claims for closure via an independent medical examination(IME), and/or to order independent medical examination in response tothe readiness prediction or recommendation.

In some embodiments, the invention takes the form of system forpredicting the outcomes of Independent Medical Examinations (IMEs) fromelectronic health records (EHR) using heuristic approaches and/ormachine learning or artificial intelligence models. One exemplary systemincludes a learning module (neural network or machine learning program)configured to learn from a set of historical IME reports, and/or theirassociated medical records how to predict the outcome of future IMEsbased only on medical records for a given injured claimant, for examplea workers compensation insurance claimant. In some embodiments, thosemedical records include data from one or more sensors worn by orotherwise associated with or cognizant of the injured claimant. (Thesensors may take the form of smartphones, smartwatches, implantableheart monitors, sensor suits, sleep monitors, pedometers, medical andnon-medical surveillance systems.)

Additionally, some embodiments include or take the form of a graphicaluser interface that presents users (for example, claims adjusters oradministrators, plaintiff attorneys, and defense attorneys) with an IMEprediction based on current medical records (or elapsed time since onsetof an injury or treatment). Some interfaces also provide a list ofrecommended IME providers for the particular injury and/or a list ofrecommend treatment providers who have a history of successfullytreating similar conditions within an acceptable time and cost ranges.Still others include integrated physician data and/or ratings forvarious criteria, such IME turn-around times, medical qualifications,patient proximity, etc.

Moreover, some embodiments feedback the results of new IMEs orderedthrough the system and/or obtained elsewhere to update training thelearning module, ensuring continually more accurate predictions of IMEand/or treatment outcomes. In some embodiments, the conclusions from abatch of one or more IMEs that were recommended by the system arecompared against the predictions. The medical data for thosemis-predicted IMEs are then tagged and processed as new training datafor one or more of the learning modules and/or classifiers in thesystem.

Some embodiments provide a software program or Software as a Serviceplatform configured to pre-screen claimant medical records and eliminateor reduce premature IMEs and the associated time and financial waste.Some systems generate an automated graphical user interface output orreport that lists one or more active insurance claims in associationwith an IME Ready or Not outcome score, prediction, or status indicator.In some embodiments, the report provides a timeline indicating when aclaimant associated with a claim file is ready to be scheduled for anIME, with reminders and/or automatic scheduling features to avoid delaysin scheduling and/or performance. The system in some variations providesuseful guideline for users (for example, claims adjusters,administrators, and attorneys) that reduces risk of ordering,scheduling, and conducting premature IMEs. Some embodiments furtherleverage the IME readiness predictions to update financial models orreserve accounts for one or more of claims in a portfolio of insuranceclaims, enabling insurers and/or insurance underwriters to moreaccurately and dynamically comply with legal reserve requirements, whileimproving overall financial strength and performance.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are described herein with reference to the followingattached figures (Figs). These figures are annotated with referencenumbers for various features and components, and these numbers are usedin the following description as a teaching aid, with like numbersreferring to the same or similar features and components.

FIG. 1 is a conceptual block diagram of an exemplary medical recordprocessing and medical examination ordering system corresponding to oneor more embodiments of the present invention.

FIG. 2 is a flow chart of an exemplary method of processing medicalrecords and/or operating a system, such as the FIG. 1 system,corresponding to one or more embodiments of the invention.

FIG. 3 is a block diagram of an exemplary graphical user interfacecorresponding to one or more embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

This document, which incorporates drawings and claims, describes one ormore specific embodiments of one or more inventions. These embodiments,offered not to limit but only to exemplify and teach the invention, areshown and described in sufficient detail to enable those skilled in theart to implement or practice the invention(s). Thus, where appropriateto avoid obscuring the invention(s), the description may omit certaininformation known to those of skill in the art.

Exemplary System

FIG. 1 is block diagram of an exemplary online IME prediction andordering system 100, for implementing one or more of the features andfunctions described above and thus corresponding to one or moreembodiments of the present invention. System 100 includes data sources110, a server 120, and an access device 130.

Exemplary Data Sources

Data sources or databases 110, which take the exemplary form of one ormore electronic, magnetic, or optical data-storage devices, include,among other things that may be implied or inferred elsewhere herein,repositories of medical records, DNA, fitness, and patient fitness orlifestyle behavioral data, de-identified (anonymized) and/or identifieddata In some embodiments, data sources 110 also includes physicianand/or other medical provider identifying and credentialling informationas well as calendaring and availability information for use in selectionand performance rating functions of one or more of the exemplaryembodiments. More specifically, data sources 110 include IME reportsdatabases 111, healthcare provider databases 112, human resource (HR)databases 113, electronic medical records (EMR) provider database 114,EMR provider database 115, industry injury statistics databases 116,FitBit personal activity sensor database 117, Apple Watch health monitordatabase 118, and other databases 119. Other datatbases may includeinsurance claim data for one or more insurance companies or self-insuredcompanies, and/or third-party administrators. In some embodiments, thedata is provided by third party platforms with the express permission oftheir users. Also in some embodiments, the data sources may includereal-time input from user-wearable sensors or other activity monitoringcameras and devices.

Data sources 110 include or are otherwise associated with respectiveindices (not shown). They are also coupled or couplable via a wirelessor wireline communications network, such as a local-, wide-, private-,or virtual-private network, to server 120, enabling data interchange viaapplication program interface, JavaScript Object Notation, or electronicdata interchange, or any convenient or desirable way of communicatingdata.

Exemplary Server(s)

Server 120, which is generally representative of one or more servers forserving data in a variety of desirable form, including for examplewebpages or other markup language forms with associated applets,remote-invocation objects, or other related software and data structuresto service clients of various “thicknesses,” for example, desk topcomputers, tablet computers, laptop computers, mobile phones, andInternet-Of-Things (IOT) devices. More particularly, server 120 includesa processor module 121, a memory module 122, with memory module 122including one or more functional modules and data structures toimplement functionality described herein,

Processor module 121 includes one or more local or distributedprocessors, controllers, or virtual machines. In the exemplaryembodiment, processor module 121 assumes any convenient or desirableform.

Memory module 122, which takes the exemplary form of one or morenon-transient electronic, magnetic, or optical data-storage devices,stores user database 123, user signup module 124, neural network ormachine learning module 125, and IME ordering module 126, as well asother functional modules to carry out one of more of the functionsdescribed herein.

User data module 123 includes user-related data and machine-executableinstructions sets for controlling, administering, and managing useraccounts and related activities conducted through system 100. Inaddition to one or more application program interfaces (APIs) (notshown) for accessing external data sources 110 or portions thereofassociated with users, user data module 123 includes user datastructures, of which data structures 1231 is generally representative.Data structure 1231 includes a user identifier portion 1231A, which islogically associated with one or more data fields or objects1231B-1231D.

Field 1231B includes account related data items, such as username,password, name, address, organizational identifier(s), credit cardinformation, social media account(s), health record accounts, fitnessand wellness monitor accounts, and access credentials, as well asaccount identifiers and access credentials for emails, calendars, webhosts, and so forth that may be used or referenced herein. Other fieldsinclude account history data, etc. Field 1231C includes organizationalor user-specific decision or recommendation threshold data governing orinfluencing operation of one or more of the AI or machine-learningclassifiers or trainers used in making predictions herein. Field 1231Dincludes service provider data, such as one or more lists of preferredservices providers or preferred service provider ratings criteria,ratings formula, ratings thresholds, or selection thresholds for one ormore medical conditions or bodily injuries.

User signup and administration module 124 includes one or more sets ofmachine readable and executable instructions, related data, andassociated graphical user interfaces for signing up new users (hosts andaffiliate medical records providers). In some embodiments, module 124also includes application program interfaces and other integrationcomponents for interacting with external third-party data platforms,such as insurance companies, third-party administrators, electronichealth records (EHR) platforms, and so forth which may be part of datasources 110.

Learning or predictor (classifier) module 125 includes machineexecutable instructions for causing one or more processors to predict orclassify one or more insurance claims, based on associated medicalrecords. The prediction or classification in some embodiments entailsdetermining a likelihood of a manual or physician conducted IME on thepatient and medical records yielded an indication that the patient hasreached maximum medical improvement or not. Additional predictions orclassifications may also be conducted.

Several exemplary classification terms are used herein for one or moreembodiments, and not necessarily all embodiments. As used in someembodiments herein, MMI is defined as “the point when an injuredpatient's or worker's healing process is not expected to further improvewith generally accepted medical treatment.” As used in some embodimentsherein, Permanent Partial Disability (PPD) is a physician's assessmentthat a patient has permanently lost some portion of the function of abody part. PPD is generally associated with a percentage disabilityrating, which is typically used to determine a monetary benefit payableto the injured person. As used in some embodiments herein, the termsInjury related, and injury not related refers to an IME physiciandetermination that the injury that the employee or more generallyinjured claimant is claiming under workers compensation insurance isrelated or not related (to their claimed inability to work or performother activities) based on the medical evidence provided, and thereforethe claimed insurance benefits are not payable to the employee. If theinjury is deemed related, benefits are payable to the employee. In someembodiments, there are four types of worker compensation benefits: (1)wage loss benefits, (2) medical benefits, (3) vocational rehabilitationbenefits, and (4) permanent partial disability (PPD) benefits.

More particularly, as FIG. 1 shows, the exemplary classification module125 is configured and/or organized into one or more machine executableinstructional sets, modules, or submodules, namely a medical recordsdata conditioner-filtering module 1251, a vectorizer module 1252, one ormore classifier modules 1253, and one or more classifier trainingmodules 1254.

Data conditioner-filtering module 1251, in some embodiments, includesmachine-executable instructions (stored in a tangible storage medium)which cause one or more processors to receive, filter, and conditiondigitized patient medical records. More specifically, this entailsreceiving deidentified digitized patient medical records for workerscomp or other types of insurance claimants, with the de-identificationbeing compliant with applicable data privacy regulations. The receiveddata is then filtered to reduce or eliminate potential noise, forexample arguably redundant or superfluous, data that may obscure moremeaningful data. To this end, some embodiments filter by selecting thefirst dated record (proximate the time of an injury associated with theclaim), a middle or intermediate record, and a last available (mostrecent) record, excluding all other records from further processing. Themiddle or intermediate record is selected by searching for a record thatdiffers substantially from or deviates from one or more of the priormedical records. (Some embodiments omit the filtering and process all orsubstantially all (for example, at least 75, 80, 85, 90, or 95% of allthe medical records presented.), in contrast to the filtering approachwhich, in some embodiments, may exclude at least 50 percent of themedical records.). Once the records are filtered in this way, theexemplary process entails conditioning the filtered medical records.Exemplary conditioning includes removing stop words, punctuation, andnumbers; tokenizing the words; converting acronyms to full lengthphrases.

Vectorizer module 1252 includes machine executable instructions forcausing one or more processors to vectorize the conditioned records in aset of data structures, known as feature vectors. In some embodiments,this entails extracting and vectorizing the conditioned set of medicalrecords into two-, three-, four-, and/or five-word phrases (N-grams),with each N-gram having a minimum document frequency approximately 10%of the number of training files. A frequency slightly under 10%, forexample 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, or 9.7, 9.8, or 9.9, or 9.95, andsetting the maximum number of features which keeps the most frequentN-grams from the training dataset (and from production datasets). Forexample, in some embodiments, the number of features is in the range of2000-2500 for document frequencies near 10% of the training datasetsize. In some embodiments, this number might increase as more trainingexamples are added. If the number of features is too low, the accuracytypically drops off rapidly. The noted parameters allow for theexemplary classifier module to look for some rarer phrases that could beimpactful in the predictions, while not overburdening it will too manyphrases that may behave generally as ‘noise’ within the data.

For example, if compilation of all N-word phrases, for example allphrases having at least two words and no more than five, that appear inat least X documents, for example eight documents, yields a total of2000 unique two-to-five word phrases, one embodiments converts eachdocument into a numerical vector that has a length of 2000. Each valuein the 2000-length feature vector represents a single phrase and thenumerical value is the TF-IDF value for that individual document. TFmeans term-frequency; IDF means inverse document frequency; and TF-IDFmeans the product of TF and IDF. If a phrase (N-gram) does not appear ina given document, the value for its location in the vector is 0. Thephrases in each vector are sorted alphabetically; so the first number ineach medical record set's vector representation will be for whichevertwo-to-five-word phrase comes first alphabetically.

In some embodiments, the medical records are designated training recordsand are manually tagged to one of two or more classifications, and thenused to train the classifier module 125. In these embodiments, themedical records are handled in similar manner as described for medicalrecords undergoing actual classification as described above.

Classifiers 1253, in some embodiments, include one or more of theclassifiers that take the form of a binary logistic regressionclassifier utilizing a sigmoid activation loss function, whereas inother embodiments with 3 or more classes, the regression classifieremploys a Softmax activation function. Some embodiments use other typesof classifiers with other types of activation functions, such asRectified Linear Unit (ReLU) activation function or Hyperbolic Tangent(tanh) function. In addition to Neural Network models, the classifiersmay take forms, such as Support Vector Machines (SVM), Naive Bayes,Decision Trees, Random Forest, and Adaptive Boosting.

Classifier training modules 1254 includes one or more sets of machineexecutable instructions for training one or more the classifier modulesused herein. In some embodiments, training one or more of the exemplaryclassifiers includes calculation of a loss function which compares eachclassifiers predictions to actual values from the training data,adjusting internal classifier model weights and/or parameters tominimize this loss in future predictions or classification. Someembodiments use a gradient descent approach to loss calculation. spaCymodels couple gradient descent with backpropagation to update the weightvalues for their RNN model when training a named-entity recognitionmodel/classifier, which is used in some embodiments to supplement theN-grams presented in the feature vectors. Alternatives to gradientdescent for minimizing the loss function include Particle SwarmOptimization, Surrogate Optimization, Simulated Annealing, StochasticGradient Descent, Nonlinear Conjugate Gradient, and Levenberg-MarquardtAlgorithm. In some embodiments, non-spaCy pre-trainednamed-entity-recognition models use other architectures such asbidirectional Long Short Term Memory models, feedforward neuralnetworks, Conditional Random Field models, and Gated Recurrent UnitNetworks for embedding.

In some embodiments, one or more of the machine-trained classifiers meetthe following requirements: Minimum Accuracy of 70-90% for each of ourmodels, for example 70%, 75%, 80%, 85%, and 90%; F1 Score of 70-90% forexample 70%, 75%, 80%, 85%, and 90%, and/or Minimum Receiver OperatingCharacteristic Area Under the Curve(ROC AUC) of 80-95%. F1 Score is ametric that looks to minimize both false positives and false negatives(in some embodiments, misclassification between classes). The exemplaryembodiment provides a training dataset balanced equally between thedifferent classes, for example half the records are MM1 and half areMM2. The same is true for the training data used for subclassificationof the MM1 and MM2.

Some embodiments use alternative classification or prediction methods,which are more heuristic in nature. For example, some embodiments use akeyword or phrasal counting method which entails comparing a givenpatient record set (which is conditioned, filtered, and/or vectorized asnoted) to first and second sets of key phrases, such as a list of MM1key phrases (those associated or highly correlated with maximum medicalimprovement) and MM2 key phrases (those associated or highly correlatedwith continued improvement likely. (In some embodiments, a thresholdcorrelation greater than 0.3, such as 0.4, 0.5, 0.6, 0.7, defines highlycorrelated.) Exemplary MMI phrases include: maximum medical improvement,physical therapy, symptoms no, manual therapy, treatment options,reviewed found negative, work activities, has negative, steroidinjection, well healed, negative bilaterally. And exemplary MM2 phrasesinclude: not present, within normal limits, external rotation,unspecified sprain, has mild, has pain, numbness tingling, complaintspain, normal range motion, mild tenderness, and has positive.

In the comparison, two counts are made, one count of the number ofmatching phrases from the first set found in the patient record set (ormore precisely feature vector representation of the record set.) and theother count of the number of matching phrases from the second set. Thegiven patient record is assigned to the classification associated withthe set having the highest count. If there is a tie in the counts or insome embodiments an insufficient difference in the two counts (forexample, less than 1%, 5%, 10%, or 20% as percentage of the total sum ofthe two counts or of the average of the two counts in some variants),then the classification is made pursuant to a stored user-selectedtie-breaker preference. For example, in some embodiments, the tiebreakeris a default selection to the MM2 (not ready for IME) classification toavoid the expense of the IME. Some embodiments use a thresholdcomparison based on the age of the claim, with claims older than athreshold age or within 1 or 5% of a threshold age, such as 6, 9, 12, or18 months (measured from date of the injury or date of the first medicaltreatment of the injury) defaulted to a maybe or an MM1 status as anudge toward conducting an IME. In some embodiments, the classificationcan be designated as indeterminant (maybe) and the injury claim markedor tagged in memory as needing a more detailed manual review.

In some embodiments, the maximum medical improvement (IME ready or IMElikely to indicate maximum medical improvement) classifier is usedexclusively to any additional classifiers, whereas in other embodiments,two or more binary or higher-level classifiers as described herein areused. For example, in some embodiments, an initial classifier determinesthe MM1 or MM2 classification, and a first set of secondary classifiersdetermines one or more MM1 subclasses and a second set of secondaryclassifiers determine one or more MM2 subclasses. For MM1 in someembodiments, patient data is classified by two secondary classifiers,the first to predict partial permanent disability (PPD) Category A(PPD=0%) or Category B (PPD>0%) and the second to predict Related Injury(1) or Not-related Injury (2). For MM2 in some embodiments, the patientdata (feature vectors representation) is passed through 5 secondaryclassifiers (models) to predict physician answers to 5 Yes/No questionsand to determine Related Injury (1) or Not-related Injury (2). Someembodiments may use a single multi-class engine to address all theprimary and secondary predictions simultaneously, whereas others may useone to address the primary and a second one to simultaneously addressall the secondary classifications. In some embodiments, the secondarymodels (classifiers) and the MM1/MM2 classifier (model 1) are the sametype of model or classifier as the primary MM1/MM2 classifier, forexample—Logistic Regression type. However, in some embodiments, thesecondary classifiers may not only differ from the primary in type, butalso from one or more of the other secondaries.

Some embodiments collect and leverage the following data in making theIME ready or not ready (that is, MM1/MM2) prediction:

-   -   Detailed job description relating to the physical demands of the        position such as    -   The specific percentage of lifting, bending and repetitive        nature of the job.    -   Nature of injury; (FROI) First Report of Injury form completed        by the injured employee.    -   Medical Treatment details (complete med records provided).    -   Claim status details—accepted or denied.    -   First Day of Lost time (if applicable).    -   Impairment rating    -   Applicable Claim related questions: Return to work status,        Medical treatment appropriateness. Nature and extent of the        claimed injury, Work restriction clarification. Maximum Medical        Improvement (MMI).

IME ordering module 126 includes data and/or machine executableinstructions for facilitating ordering independent medical examinationsor evaluations and/or generating and presenting graphical user interfacepre-examination reports based on classifier outputs. More specifically,module 126 includes an ordering module 1261 and a pre-examination reportgenerator module 1262. Ordering module 1261 is configured to generateGUIs for managing claim and presenting those claims and classifierassociated classifier outputs and medical data, and pre-examination andfull IMEs in the context of an ordering system. (See below for furtherdetails on an exemplary forms and functionality.)

Pre-examination report generator 1262 generates pre-examination reportsbased on provided medical data and one or more classification orprediction outputs of classifier module 125. In some variants, module1262 includes a separate set of report generation classifiers organizedusing multi-class classification scheme, Boolean method scheme, or textsummarization. The multi-class classification system uses results of theNER module in conjunction with several specialized classifiers to fillin an IME report template framework or data structure. For example, oneor more of the constituent classifiers determine, based on thenamed-entity-extended medical record vectors, which body part(s) weremost likely to be injured, what treatments/diagnostic tests have beenperformed and the results, and/or the possible causes of the injury.Some embodiments also include date extraction functions to associatenamed entities, and events with dates, thus enabling generation of atimeline of the claimant patient's treatment and progress. The outputpre-examination report follows a formulaic structure that presents themodel's prediction for each relevant classifier. For example, someembodiments of a graphical user interface pre-examination report,present a structure, such as

-   -   “The patient is being seen for an injury to their body part 1.        The injury occurred on date 1, which is time calculation 1 days        ago.” The patient has received treatment 1 a total of treatment        1 count during this period, and has more likely than not reached        MM1 or not reached MM1 with a confidence rating 1,”        with the body part 1, date 1, treatment 1, treatment 1 count        fields determined by associated classifiers, and the time        calculation 1 determined as a function of date 1, and confidence        rating 1 being part of the output of classifier 125. For Boolean        method variants, the pre-examine report generator predict        answers to True/False or Yes/No questions concerning why a        particular EHR classification, MM1 or MM2 for example, was        applied in each case. This architecture includes a set of        classifier models to answer a pre-determined group of        questions/reasons about what lead to a certain prediction.        Examples include Length of employment appropriate to develop        symptoms, Job description appropriate to develop symptoms, and        Pre-existing Condition contributed. The output from the        pre-examine report generator would list each question/reason        followed with a True/False or Yes/No as an explanation for how        that question/reason played a role in the reason for MM1 or MM2        prediction. The various classification models used to answer        each question/reason operate like one or more of the binary        classifiers previously described.

For text summarization variants, the model condenses the EHR text intothe most relevant information to answer the most important questionsconcerning the MM1/MM2 classification results. This can be performed viaabstractive or extractive summarization techniques. Abstractive TextSummarization entails combing the EHR inputs to find the relevantinformation and provide an GUI report comprising sentences one or moresentences that are not quoted directly from the EHR. The output would beunique from the EHR provided. Extractive Text Summarization entailsextracting actual sentences from the EHR to create the output. Someembodiments take a hybrid form, combining Abstractive and ExtractiveText Summarization to create the report. In some variants, this is asimple combining of the two summarization results, whereas in others,one or more of the extractive text summarization sentences can be fedinto the abstractive summarization to generate abstractive text. Eachsummarization in various embodiments uses deep learning techniques toconstruct the output. Different algorithms for text summarizationinclude weighting sentences by word frequencies, TextRank, RecurrentNeural Networks encoder-decoder models, and sentence embeddings.

Some embodiments use provided IME summaries for validating thepre-examination report generator model, with the primary evaluationmetric being the ROUGE score. ROUGE stands for Recall-OrientedUnderstudy for Gisting Evaluation, and computing it entails comparingthe machine learning output to human-produced reference summary (theIME) by looking at the matching overlap of N-grams between the twosummaries.

IME ordering module 126 provides its graphical user interface structuresand other outputs to one or more access device, such as access device130, for an insurance claims and/or other professionals, such as alawyer, paralegal, administrative law judge, and/or to a patientconcerned about whether a second or third medical opinion should beordered.

Exemplary Access Device(s)

Access device 130, which is generally representative of one or moreaccess devices, takes the exemplary form of a personal computer,workstation, personal digital assistant, mobile telephone, kiosk, or anyother device capable of providing an effective user interface with aserver or database. Specifically, access device 130 includes a processormodule 131, a memory 132, a display 133, a keyboard 134, and a graphicalpointer or selector 135. (In some embodiments, display 133 includes atouch screen capability.)

Processor module 131, which includes one or more processors, processingcircuits, or controllers, is coupled to memory 132. Memory 132 storescode (machine-readable or executable instructions) for an operatingsystem 136, a browser 137, and a graphical user interface (GUI) 138(defined in whole or part by various modules within server 120). In theexemplary embodiment, operating system 136 and browser 137 not onlyreceive inputs from keyboard 134 and selector 135, but also supportrendering of GUI 138 on display 133. Upon rendering, GUI 138, shown ondisplay 133 as GUI 138′, presents data in association with one or moreinteractive control features (or user-interface elements). In theexemplary embodiment, each of these control features takes the form of ahyperlink or other browser-compatible command input and provides accessto and control of various regions of the graphical user interfacesdescribed herein. More particularly, GUI 138 includes, among otherthings, regions for users, such as claims adjusters, to access thesystem and request and receive reports on groups or individual worker'scompensation claims, which are governed by and interact with variousportions of server 120. GUI 138 may include a IME prediction region 1381listing IME predictions for one or more claimants, a list 1382 of one ormore recommended IME providers for one or more of the affirmative IMEpredictions (that is, predictions that actual IMEs will indicateclaimant worker is ready to work or return to other activitiespreviously constrained or prevented by injury under treatment); and/or alist 1383 of recommended or ranked or medical examination or treatmentproviders who may be at lower cost and/or shorter positive outcome timefor the injury of one or more of the IMEs having a negative IMEprediction.

Exemplary Method(s) of Operation

FIG. 2 shows a flow chart 200 of one or more exemplary methods ofoperating a medical records processing and/or medical examinationprediction and ordering system, such as system 100. Flow chart 200includes blocks or steps 202-229, which are arranged and described as asequence in the exemplary embodiment for sake of explanatory clarity andconcision. However, other embodiments may change the order of two ormore of the blocks or execute two or more of the blocks in parallel, forexample to facilitate processing of batches of insurance claims ormedical data. Moreover, still other embodiments implement the blocks astwo or more interconnected hardware modules with related control anddata signals communicated between and through the modules. Thus, theexemplary process flow applies to software, hardware, and firmwareimplementations.

At block 202, the exemplary method begins with the receipt of one ormore filed insurance claims. In some embodiments, the claims concernbodily injury and are logically associated with one or more types ofinsurance policies, such as commercial general liability, automobileliability, and/or workers compensation insurance. In some embodiments,the claims may pertain to legal claims, such as negligence or medicalmalpractice, or other bodily injury claims. In still other embodiments,the claims may pertain to other types of insurance claims where expertopinions are desired to substantiate eligibility or ineligibility ofbenefit payment. (Some embodiments may implement one or more portions ofthe exemplary systems or methods herein for use by health care consumersto provide recommendations for second opinions on their own medicaltreatments or those of a friend or loved one who have granted themaccess to relevant medical records or data.) Exemplary executioncontinues at block 204.

At block 204, the exemplary method begins with receipt of one or moresets or batches of electronic medical records. In some embodiments, thisentails establishing a secure wide-area-network connection, for examplevia the Internet, with a database owned or controlled by an independententity, such as an insurance company, a third-party administrator, ahealthcare provider, or a third-party electronic health recordaggregator. In some embodiments, the records are stored by the sameentity operating the system. For data privacy and security, the recordsare generally provided in a deidentified form to ensure compliance withHIPAA. Execution continues at block 206.

Block 206 entails filtering and conditioning one or more portions of thereceived electronic medical records. In the exemplary embodiment, thefiltering and conditioning are performed by a module such module 1251 inFIG. 1 . Generally, this conditioning entails eliminating stop wordsand/or other words and phrases deemed irrelevant to the informationvalue of each record and then filtering the records themselves. Thefiltering entails selecting the first dated record (proximate the timeof an injury associated with the claim), one or more middle orintermediate records, and a last available (most recent) record from theset of available records, with the middle or intermediate recordsselected by searching for a record that differs substantially from ordeviates from one or more of the prior medical records, generally thepreceding record when one considers the records as a temporal sequence.(Some embodiments omit the filtering and process all or substantiallyall (for example, at least 75, 80, 85, 90, or 95% of all the medicalrecords presented.) In some embodiments, the data is also passed througha named-entity-recognition process, which entails identifying bodyparts, treatment events, dates, etc. within the set of medical recordsfor each claimant/patient. Execution then proceeds to block 208.

Block 208 entails vectorizing the filtered and/or conditioned data, forexample using module 2512 in FIG. 1 , producing a set of vectorsrepresentative of the data, with each vector generally representative ofthe medical data associated with a corresponding one of the insuranceclaims. Some embodiments restrict each vector to a specific bodilyinjury in a claim. (In the case of multiple injuries per claim, thefiltering or vectorizing using the named entity recognition enginefacilitates parsing records according to primary body part undertreatment.) The exemplary vectorization process, as detailed previously,entails identifying two-, three-, four-, and/or five-word N-grams, witheach N-gram having a minimum document frequency, for example 10% of thenumber of training files used to create the classifier (for machinelearning implementations). For example, in some embodiments, the numberof features is in the range of 2000-2500 for document frequencies near10% of the training dataset size. Execution continues at block 210.

Block 210 classifies the medical data associated with each claim, thatis, the feature vector for each claim, into one or more categories. Inthe exemplary embodiment, this entail classifying each feature vectorusing one or more the classification procedures associated withclassifier 1254 in the FIG. 1 system. As noted, this classifier may takethe form of one or more binary linear regression classifiers utilizing,for example, a sigmoid, Softmax, Rectified Linear Unit (ReLU), orHyperbolic Tangent (tanh) activation function. The result of theclassification operation is that one or more of the feature vectors (andassociated insurance claims) are tagged or marked with a date stampedclassification as MM1 or MM2, where MM1 denotes the prediction that anactual physician conducted IME is likely to indicate that the injuredclaimant has reached maximum medical recovery or improvement for theclaimed injury based on standard medical care and that MM2 denotes theprediction that an actual IME would indicate that the injured claimanthas not reached maximum medical recovery. Execution continues at block212, where execution branches to block 214 for claims that areclassified MM1 and to block 224 for claims that are classified MM2.

At block 214, the exemplary method entails ordering an IME for one ormore of the claimants associated with MM1 classified claims and/orgenerating a pre-examination report. In some embodiments, this orderingentails server 120 (in FIG. 1 ) defining and presenting via a graphicaluser interface (or portion thereof) on one or more access devices, suchas device 130, a list of one or more claim identifiers and theassociated classification or classification-related rankings (orpredictions and prediction-related rankings.) For example, FIG. 3 showsan interactive graphical user interface GUI 300 used in someembodiments.

More particularly, GUI 300 includes a claim listing region 310, a claimdetail region 320, and an IME order region 330. Claim listing region 310displays a listing 312 of one or more selectable claim identifiers ID1,1D2, . . . , 1D7 and a listing 314 of corresponding associated rankingor classification indicator field, generally designated CC in thefigure. The claims are presented in an order or sequence based on itsIME readiness classification or classification strength score asdetermined at block 210. In some embodiments, a trinary presentation ofthe classification is used, indicating one of three states: Y for readyfor IME, N for not ready for IME, and M for maybe ready for IME. Someembodiments may omit the maybe state, whereas others may present anumerical score of readiness or classification confidence for the IMEready state. Claim detail region 320 which displayed in response toselection of a particular listed claim within region 310, for example,ID3 as shown, presents further information about the claim. Inparticular, region 320 includes a synopsis or diagnosis region 321, aconfidence indication region 322, and a medical data access region 323.Synopsis region 321 identifies the body part or medical conditionassociated with the claim, for example, carpel tunnel or back injury. Insome embodiments, this region includes one or more selectable portionsto receive further information about the given diagnosis, such asaverage recovery time for the injury, and/or average costs of suchclaims, and/or one or more ‘red flag’ informational items for a claimsadjuster to beware of in assessing the claims. Confidence indicationregion 322 provides an indication of strength of the classificationindication provided by at block 210 (In FIG. 2 .). Medical data accessregion 322 is user selectable to provide further display of theunderlying medical records associated with the selected claim in region310.

In response also to selection of a particular claim, IME order region330 provides a ranked listing 331 of one or more qualified IMEphysicians who are available within a predetermined time period, such asseven calendar or business days, for conducting an IME of the claimantassociated with the claimant for the selected claim. In someembodiments, the calendar availability integrated into the list ensuresthat the physician or more generally expert service provideravailability is correlated with actual claimant availability. In otherwords, physician availability times and check against patientavailability times to ensure that both are available. Some embodimentsalso include data presentation or display columns 332, 333, 334, and335, and an order command feature 336. Column 332 include one or moreselectable physician identifier labels or indicators, such as Dr. A, Dr.B, Dr. C, each of which identifies a particular physician or otherexpert service or second opinion provider (more generally a serviceprovider). Column 333 provides rating or ranking for one or more of thephysicians or providers, for example R1-R5. In some embodiments, theseratings are based on credentials and years of experience of theassociated physician or expert provider. In some embodiment, the ratingis based on formulaic score involving multiple criteria, and in othersit's based on average of claim adjuster and/or patient rankings on a5-star, 10-point, or 100-point scale. Column 334 provides pricinginformation indicators P1-P5 associated with each of the listed servicesproviders, each indicating a specific price quote or hourly rate forcompletion of an IME for a patient with the indicated diagnosis. In someembodiments, the price quotes are provided in response to activesolicitation of bids in real-time or at the time of claim filing from anopen or curated marketplace of expert providers. Column 335 providesinformation about average turn-around times T1-T5 for physicians (orother expert) between completing a physical examination and submittingan IME report based on the examination. Order command feature 336includes a selection or highlight window 3361 which a user positionsover at least one of the presented expert options and an order commandbutton 3362 which is selectable by a user to initiate or place an orderfor an IME of the given claimant/patient at the next available time sloton the physician and/or patient availability calendar within thepredetermined examination window. In some embodiments, initiation orplacement of the order causes automatic transmission of HIPPA compliantmedical data and calendared appointment (such as iCal) and electronicpurchase order and/or partial payment to the associated physician orexpert provider. Some embodiments also electronically transmit thecalendared appointment to the insurance claimant. In some embodiments,the ordering and appointment messaging are also handled or mirroredinternally within a messaging function for system 100, which providednotifications of new messages to system users, such as claims adjusters,medical service providers, and insurance claimants. Also, someembodiments allow claim adjuster users to select two or more medicalservice providers and initiate an auction or Request for Proposal orQuotes for providing an IME for one or more claimants.

Some embodiments include an ordering disable feature, which preventsadjusters from ordering IMEs for claimants for which there is not asufficiently strong MM1 prediction or classification. To this end, claimadjuster user data includes an associated MM1 classification orprediction confidence threshold that gates his or her access to theorder control feature. In some variants, the order control feature isaltered in color to indicate that it has been disabled for a particularclaim and/or adjuster, or to indicate that a managerial access PIN(personal identification number) or other access key, for example a4-digit code or an 8-character password, may be required to be enteredafter invoking the order control feature to complete the ordering. Insome embodiments, a claims adjuster seeking authorization would invokean integrated messaging function, which would automatically queue up theorder as a pending order requiring approval prior to transmission to aservice provider for completion. In this instance, a supervisory claimsadjuster would access a pending order portion of the graphical userinterface and have authority to approve or disapprove of the pendingorder, with approved orders proceeding on for completion, anddisapproved orders not.

The exemplary ordering process also automatically updates the data fileor diary for the associated claim to indicate the date and time of theorder, identity of the physician or expert, and expected date ofsubmission of the IME report. Some embodiments also set automatic followup messages, such as emails or texts or SMS messages, to the physicianor expert if IME reports are not received during expected time frames.In some embodiments, some portion of physician payments are madeautomatically in response to submission and/or approval of the submittedIME reports. (Some embodiments may provide an online IME reportauthoring interface which provides a summary of the relevant medicalrecords of the claimant for use of the physician, as well as questions(see questions listed in provisional patent application) to be answeredin the IME report, streamlining IME report generation and incentivizingexpert to join the open or curated marketplace.)

FIG. 2 shows that after block 214, exemplary execution advances to block216, which entails review of one or more ordered IME reports. In someembodiments, this entails a manual review by claims adjuster to ensurecompleteness of the report. Some embodiments also include an automatedIME report analyzer to ensure completeness and plausibility of thereport. For example, the analyzer may include a binary pass-failclassifier trained on acceptable IME reports to ensure accuracy andcompleteness of the report, for example based on comparison to thepre-examination report generated at 214. Also, some embodiments employ amulti-class A, B, C, D, F grading classifier to assess the accuracy andcompleteness of the report by assigning an actual grade of the report.In embodiments employing an IME report analyzer, the result of theanalysis classification, whether pass-fail or A-F grade, is added to adata record associated with the relevant physicians or expert, for usein further ordering decisions. IME reports that are determined to beunacceptable are generally returned to the authoring physician forcorrection or rework. For claims having acceptable IME reports,execution continues at block 218.

Block 218 entails determining whether to initiate one or more claimclosures based on the results of one or more corresponding acceptableIME reports. (In a batch processing mode, multiple closures and multiplecorresponding reports would be proceeding in parallel or sequentiallyand then queued up for batch handling by one or more adjusters.) In someembodiments, this is a manual process conducted by a claims adjuster.Some other embodiments, however, provide an automated or function, forexample facilitated by a graphical user interface used by an adjuster.More particularly, if one or more accepted IME reports indicate thatmaximum medical improvement has been achieved, as judged by thephysician who completed and signed off on the IME reports, executionadvances to block 220 to initiate closure of the claim, or to block 224.

Block 220 entails automatically or manually updating or schedulingautomatic update one or more insurance reserve accounts to reflectimminent or anticipated closure of one or more insurance claims. Morespecifically, this generally entails generation and transmission ofEnd-Of-Benefits notification letters or communications to the affectedclaimants and release of insurance reserve funds set aside to cover theassociated claims within statutorily defined time frames, therebyreducing legal risks associated with having insufficient reserves, whilealso potentially allowing more efficient use of the released capital foroperations, for benefits, for premium reductions, and/or investment.Execution continues at block 222.

Block 222 entails tagging or marking the medical records data and/orassociated vector representations for the claims deemed ready forclosure as MM1 records. Execution than advances to block 226 fortraining of the classifier based on the marked data.

If block 218 determines based on the one or more physician received IMEreport that one or more of the claims are not ready for closure,execution branches to block 224, which entails marking the medicalrecords data and/or associated vector representations as MM2. Thismarked data is then presented to block 226 for retraining of one or moreof the classifiers based on the MM2 marked data. Execution thencontinues at block 229 which represents a return to block 202 forreceipt of new insurance claims.

FIG. 2 shows that if block 212 indicates that for claims classified asMM2, execution branches to block 227, instead of block 214.

Block 227 entails further classifying MM2 records according to one ormore subclassifications. In the exemplary embodiment, this entails useof one or more binary classifiers similar in structure to the binaryclassifiers describe above. In particular, one MM2 subclassifierclassifies the medical record data as related or unrelated to the injuryfor which benefits are claimed. If this classification indicates thatthe benefits claim is not related to the injury, some embodiments omitthe other classifications and proceed to block 229. However, someembodiments continue with one or more of the following binaryclassifiers:

-   -   1) Current Activity Restrictions Appropriate or Inappropriate        for the claimed injury:    -   2) Current Medical Treatment Appropriate or Inappropriate for        the claimed injury:    -   3) Recommended Treatment Appropriate or Inappropriate for the        claimed injury;    -   4) Work Disability Appropriate or Inappropriate for the claimed        injury. Execution then advances to block 228.

Block 228 entails determining whether the status of the claim is properor not, that is, is the claimant receiving appropriate medical treatment(and/or benefits). In the exemplary embodiment, this entails assessingthe results of the MM2 classifications. Specifically, if any of the MM2classifications indications or predictions indicate that a reasonablyskilled or suitably expert physician would likely consider the medicaltreatment benefit inappropriate, then execution branches to block 214with recommendation or assessment indicator that an IME be ordered forthe claimant. If none of the MM2 subclassifiers indicate impropriety inthe claim medical handling, then execution continues at block 229,which, as noted previously, entails returning to block 202 for receiptof new claims for processing.

CONCLUSION

In the foregoing specification, specific exemplary embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and FIGS. are to be regarded in an illustrative ratherthan a restrictive sense, and all such modifications are intended to beincluded within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover, in this document, relational terms, such as second, top andbottom, and the like may be used solely to distinguish one entity oraction from another entity or action without necessarily requiring orimplying any actual such relationship or order between such entities oractions. The terms “comprises,” “comprising,” “has”, “having,”“includes”, “including,” “contains”, “containing” or any other variationthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, article, or apparatus that comprises, has, includes,contains a list of elements does not include only those elements but mayinclude other elements not expressly listed or inherent to such process,method, article, or apparatus. An element proceeded by “comprises a”,“has . . . a”, “includes . . . a”, “contains . . . a” does not, withoutmore constraints, preclude the existence of additional identicalelements in the process, method, article, or apparatus that comprises,has, includes, contains the element. The terms “a” and “an” are definedas one or more unless explicitly stated otherwise herein. The terms“substantially”, “essentially”, “approximately”, “about” or any otherversion thereof, are defined as being close to as understood by one ofordinary skill in the art, and in one non-limiting embodiment the termis defined to be within 10%, in another embodiment within 5%, in anotherembodiment within 1% and in another embodiment within 0.5%. The term“coupled” as used herein is defined as connected, although notnecessarily directly and not necessarily mechanically. A device orstructure that is “configured” in a certain way is configured in atleast that way but may also be configured in ways that are not listed.Also, the term “exemplary” is used as an adjective herein to modify oneor more nouns, such as embodiment, system, method, device, and is meantto indicate specifically that the noun is provided as a non-limitingexample.

1-30. (canceled)
 31. A system comprising a non-transitorymachine-readable storage medium having instructions stored thereon,which, when executed by one or more processors, cause the system to:obtain a first set of electronic medical records for one or moreclaimants associated with corresponding filed insurance claims forcorresponding bodily injuries, extract a first set of one or morefeatures from the first set of medical records; feed the first set ofone or more features into a neural network responsive to the first setof one or more features to output and store in a memory on acommunications network a maximum medical improvement prediction for atleast a first one of the claimants; generate at least a portion of agraphical user interface display having an insurance claim displayregion, the claim display region including identifier indicia associatedwith the insurance claim of the one insurance claimant, an indication ofthe maximum medical improvement prediction for the one insuranceclaimant, and a first user selectable control feature operativelyassociated with the second insurance claimant for causing display of alist of one or more physician identifiers for physicians determined tobe qualified and available to perform an independent medical examinationof the one insurance claimant based on the indication of the maximummedical improvement prediction, and a second user selectable controlfeature for electronically ordering an independent medical examinationof the one insurance claimant from a selected one of the listedphysicians.
 32. The system of claim 31, wherein the instructions alsocause one or more of the processors to exclude one or more medicalrecords from each of the first sets of medical records from affectingthe maximum medical improvement prediction.
 33. The system of claim 31:wherein the first set of medical records for each correspondinginsurance claimant define a corresponding temporal record sequence; andwherein the instructions further cause one or more of the processors toprevent one or more of the medical records that do not differsufficiently from an immediately preceding medical record within itscorresponding temporal record sequence from affecting the maximummedical improvement prediction.
 34. The system of claim 31, wherein thegraphical user interface display presents a representation of aprediction confidence indication in association with the claimidentifier indicia for the one insurance claimant.
 35. The system ofclaim 31, wherein each listed physician in the graphical user interfacedisplay has a predetermined availability to conduct the independentmedical examination within a predetermined time frame, such as sevendays, from a current date of user selection of the first user selectablecontrol feature.
 36. The system of claim 31, wherein the instructionsfurther cause one or more of the processors to: obtain a second set ofelectronic medical records for two or more claimants associated withcorresponding filed insurance claims for corresponding bodily injuries,the medical records for each claimant having a corresponding maximummedical recovery status label indicating affirmatively or negativelywhether the records represent maximum medical recovery for his or herbodily injury; extract a second set of one or more features from thesecond set of medical records; and train the neural network based on thesecond set of one or more features and corresponding maximum medicalrecovery status labels.
 37. The system of claim 31, wherein theinstructions further cause one or more of the processors to cause theneural network to output a prediction of whether a human physician wouldregard the medical records as indicating that the one claimant hasexperienced proper or improper medical treatment for his or her bodilyinjury.
 38. The system of claim 37, wherein the proper or impropermedical status prediction predicts whether a physician conducting anindependent medical examination would consider current activityrestrictions for a claimant proper or not; or current medical treatmentfor the claimed injury proper or not; or a recommended treatment for theclaimed injury proper or not; or work disability for the claimed injuryproper or not.
 39. The storage medium of claim 31, wherein the neuralnetwork algorithm has been trained using supervised training.
 40. Anon-transitory machine-readable storage medium having instructionsstored thereon, which, when executed by one or more processors, causethe system to: obtain a first set of electronic medical records for oneor more claimants associated with corresponding filed insurance claimsfor corresponding bodily injuries, extract a first set of one or morefeatures from the first set of medical records; feed the first set ofone or more features into a neural network to output a prediction ofwhether a physician would deem the medical records as indicating thatthe claimant has achieved maximum or sub-maximum medical recovery andthus is likely ineligible or likely eligible for continued insurancebenefits for the injury.
 41. The storage medium of claim 40, wherein theinstructions also cause one or more of the processors to exclude one ormore medical records from each of the first sets of medical records fromaffecting the assessment.
 42. The storage medium of claim 40: whereinthe first set of medical records for each corresponding insuranceclaimant define a corresponding temporal record sequence; and whereinthe instructions further cause one or more of the processors to preventone or more of the medical records that do not differ sufficiently froman immediately preceding medical record within its correspondingtemporal record sequence from affecting the output of the neuralnetwork.
 43. The storage medium of claim 40, wherein the neural networkalgorithm has been trained using supervised training.
 44. The storagemedium of claim 40, wherein the instructions further cause one or moreof the processors to: generate at least a portion of a graphical userinterface display having an insurance claim display region, the claimdisplay region including identifier indicia associated with theinsurance claim of the insurance claimant, an indication of the maximummedical improvement prediction, and a first user selectable controlfeature operatively associated with the second insurance claimant forcausing display of a list of one or more physician identifiers forphysicians determined to be qualified and available to perform anindependent medical examination of the second insurance claimant withina predetermined time period, and a second user selectable controlfeature for electronically ordering an independent medical examinationof the second insurance claimant from a selected one of the listedphysicians.
 45. The storage medium of claim 44, wherein each listedphysician in the graphical user interface display has a predeterminedavailability to conduct the independent medical examination within apredetermined time frame, such as seven days, from a current date ofuser selection of the first user selectable control feature.
 46. Themedium of claim 42, wherein the instructions further cause one or moreof the processors to cause a neural network to output a prediction ofwhether a physician would regard the medical records as indicating thata proper or improper medical status.
 47. The storage medium of claim 46,wherein the proper or improper medical status prediction predictswhether a physician conducting an independent medical examination wouldconsider current activity restrictions for a claimant proper or not; orcurrent medical treatment for the claimed injury proper or not; or arecommended treatment for the claimed injury proper or not; or workdisability for the claimed injury proper or not.
 48. A non-transitorymachine-readable storage medium having instructions stored thereon,which, when executed by one or more processors, cause the system to:obtain a first set of electronic medical records for one or moreclaimants associated with corresponding filed insurance claims forcorresponding bodily injuries, extract a first set of one or morefeatures from the first set of medical records; feed the first set ofone or more features into an artificial intelligence to output two ormore physician predictions, with at least of the predictions beingwhether a physician would deem the medical records as indicating thatthe claimant has achieved maximum or sub-maximum medical improvement andat least one of the predictions being whether a physician would considercurrent activity restrictions for a claimant proper or not, or currentmedical treatment for the claimed injury proper or not, or a recommendedtreatment for the claimed injury proper or not, or work disability forthe claimed injury proper or not.
 49. The storage medium of claim 48,wherein the instructions further cause one or more of the processors to:generate at least a portion of a graphical user interface display havingan insurance claim display region, the claim display region includingidentifier indicia associated with the insurance claim of the insuranceclaimant, an indication of the maximum medical improvement prediction,and a first user selectable control feature operatively associated withthe second insurance claimant for causing display of a list of one ormore physician identifiers for physicians determined to be qualified andavailable to perform an independent medical examination of the secondinsurance claimant within a predetermined time period, and a second userselectable control feature for electronically ordering an independentmedical examination of the second insurance claimant from a selected oneof the listed physicians.
 50. The storage medium of claim 49, whereinthe artificial intelligence comprises at least one supervised trainedneural network or at least one binary logistical regression algorithm.51. The storage medium of claim, wherein the instructions also cause oneor more of the processors to exclude one or more medical records fromeach of the first sets of medical records from affecting at thepredictions.